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A Tool Condition Monitoring Study To Support Circular Economy
No full textCNC (Computer Numerical Control) machines are vital for precision and efficiency in manufacturing but are prone to tool wear, causing disruptions and sustainability challenges. This study introduces a project aimed at sustainable CNC tool management within the circular economy framework, focusing on extending tool lifespan through predictive analytics. Real-time monitoring predicts optimal tool replacement times, promoting reuse, repair, and recycling. The methodology includes data collection, preprocessing, anomaly detection, real-time analysis, and machine learning model selection, with the Random Forest model proving most effective. Unique contributions include the integration of advanced sensor data with AI-driven predictive maintenance, and the application of circular economy principles to CNC tool management. The results highlight significant accuracy in tool condition categorization, contributing to waste reduction and sustainable practices in manufacturing.This study was conducted by TEKNOPAR and supported partially by the DigiPrime project. DigiPrime was funded by the European Union's Horizon 2020 research and innovation programme under GA No. 873111European Union [873111
İkinci El Çevrimiçi Müşteri Ve Satıcı Etkileşimi
No full textPazarlama ve Pazarlama Araştırmaları Derneği 27. Pazarlama Kongresi Erzurum 30 Mayıs – 01 Haziran 2024 Pazarlamada Yapay ZekaBu çalışmanın amacı, çevrimiçi ikinci el alışverişte, müşteri etkileşimini sağlayan faktörlerle ilgili keşifsel bir analiz yapmayı amaçlamaktadır. Çevrimiçi ikinci el pazarlarda tüketiciler hem alıcı hem de satıcı olabilmektedir. Alıcı motivasyonları, değerleri ve davranışları çokça çalışılmış olmasına karşın, ikinci el satıcıların çevrimiçi etkileşime olan katkıları üzerinde sınırlı sayıda çalışma mevcuttur. Bu çalışmada müşteri etkileşimi odaklı bir model, 322 kişiye uygulanan bir anket ile test edilmiştir. Elde edilen bulgulara göre ürün hakkında verilen yeterli bir açıklama, müşteri etkileşimini artırmaktadır. Ancak satıcının etkileşimine yönelik birikimi, ürün bilgisinin yarattığı etkiye aracılık yapmaktadır. Yani satıcı etkileşimi yüksek ise, bunun müşteri etkileşimine etkisi daha yüksek gözükmektedir. Bu bulgulara göre müşteri etkileşiminin tek başına ele alınmadan satıcı etkileşimi bağlantısı kurularak tartışılması gerekli görülmektedir.The aim of this study is to conduct an exploratory analysis on the factors that enable customer interaction in online second-hand shopping. In online second-hand markets, consumers can be both buyers and sellers. Although buyer motivations, values and behaviors have been widely studied, there are limited studies on second-hand sellers' contributions to online interaction. In this study, a customer engagement model was tested with a survey administered to 322 people. According to the findings, an adequate explanation about the product increases customer engagement. However, the seller's engagement experience mediates the effect created by product knowledge. In other words, if seller engagement is high, its effect on customer engagement seems to be higher. According to these findings, it seems necessary to discuss customer engagement by establishing a connection with seller engagement, rather than considering it alone
Epidemiological Characteristics of Inflammatory Bowel Diseases in the Last Decade: Multi-Center Turkiye Data
No full text[No abstract available
Determination of the Relative Sign of the Higgs Boson Couplings To i>w/I> and i>z/I> Bosons Using i>wh/I> Production Via Vector-Boson Fusion With the Atlas Detector
No full textStanislaus, Beojan/0000-0001-9007-7658; D'Auria, Saverio/0000-0003-3393-6318; ABREU, Henso/0000-0002-1599-2896; Grabowska-Bold, Iwona/0000-0001-9159-1210; Camplani, Alessandra/0000-0002-6386-9788; Haley, Joseph/0000-0002-6938-7405; Etzion, Erez/0000-0001-6871-7794; Oh, Alexander/0000-0001-9025-0422; Butterworth, Jonathan/0000-0002-5905-5394; Abramowicz, Halina/0000-0001-5329-6640; Gonnella, Francesco/0000-0003-0885-1654; Tian, Yusong/0000-0001-8739-9250; Abbott, Braden/0000-0002-5888-2734; Balek, Petr/0000-0002-0942-1966; Stabile, Alberto/0000-0002-6868-8329; KHWAIRA, Yahya/0000-0001-8538-1647; Calafiura, Paolo/0000-0002-1692-1678; Abulaiti, Yiming/0000-0003-0403-3697; Staszewski, Rafal/0000-0001-7708-9259; Mlinarevic, Marin/0000-0003-3587-646X; Fernandez-Martinez, Pablo/0000-0002-7818-6971; Bold, Tomasz/0000-0002-2432-411X; /0000-0001-5765-1750; Dyndal, Mateusz/0000-0001-9632-6352; Kupco, Alexander/0000-0003-3692-1410; Potepa, Patrycja Anna/0000-0002-1325-7214; Kretzschmar, Jan/0000-0002-8515-1355; Konstantinidis, Nikolaos/0000-0002-4140-6360; Petersen, Troels/0000-0003-0221-3037; Dingfelder, Jochen/0000-0001-5767-2121; Abicht, Nils Julius/0000-0001-5763-2760; Gwilliam, Carl/0000-0002-9401-5304; Mitsou, Vasiliki A./0000-0002-1533-8886; Ragusa, Francesco/0000-0002-4064-0489; Mindur, Bartosz/0000-0002-5511-2611; Mazzeo, Elena/0000-0002-8406-0195; Aad, Georges/0000-0002-6665-4934; Aboulhorma, Asmaa/0000-0002-9987-2292; Fiorini, Luca/0000-0002-5070-2735; Valenzuela Castillo, Franchesco Adrino/0009-0007-0110-3852; Rompotis, Nikolaos/0000-0003-2577-1875; Ventura, Andrea/0000-0002-3368-3413; Carbone, Antonio/0000-0002-4117-3800; McKee, Shawn/0000-0002-4551-4502; Dabrowski, Wladyslaw/0000-0001-9061-9568; Teixeira-Dias, Pedro/0000-0001-9977-3836; Maj, Klaudia/0000-0003-4819-9226; de la Torre Perez, Hector/0000-0002-4516-5269; Pintucci, Laura/0000-0001-9842-9830The associated production of Higgs and W bosons via vector-boson fusion is highly sensitive to the relative sign of the Higgs boson couplings to W and Z bosons. In this Letter, two searches for this process are presented, using 140 fb(-1) of proton-proton collision data at root s = 13 TeV recorded by the ATLAS detector at the LHC. The first search targets scenarios with opposite-sign couplings of the W and Z bosons to the Higgs boson, while the second targets standard model-like scenarios with same-sign couplings. Both analyses consider Higgs boson decays into a pair of b quarks and W boson decays with an electron or muon. The data exclude the opposite-sign coupling hypothesis with a significance beyond 5 sigma, and the observed (expected) upper limit set on the cross section for vector-boson fusion WH production is 9.0 (8.7) times the standard model value at 95% confidence level.We thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide without whom ATLAS could not be operated efficiently. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide, and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [44]. We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC, and CFI, Canada; CERN; ANID, Chile; CAS, MOST, and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF, and Cantons of Bern and Geneva, Switzerland; MOST, Taipei; TENMAK, Turkiye; STFC, United Kingdom; DOE and NSF, USA. Individual groups and members have received support from BCKDF, CANARIE, CRC, and DRAC, Canada; CERN-CZ, PRIMUS 21/SCI/017, and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU, and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex, and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales, and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT 1230987); China: National Natural Science Foundation of China (NSFC-12175119, NSFC 12275265, NSFC-12075060); Czech Republic: PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC-101002463); European Union: European Research Council (ERC-948254), Horizon 2020 Framework Programme (MUCCA-CHIST-ERA-19-XAI-00), European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU PE00000013), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU), Marie Sklodowska-Curie Actions (EU H2020 MSC IF Grant No. 101033496); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022), Investissements d'Avenir Idex (ANR-11-LABX-0012), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG-469666862, DFG-CR 312/5-1); Italy: Istituto Nazionale di Fisica Nucleare (FELLINI G.A. n. 754496, ICSC, NextGenerationEU); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP21H05085, JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020-VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472); Poland: Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187); Slovenia: Slovenian Research Agency (ARIS grant J1-3010); Spain: BBVA Foundation (LEO22-1-603), Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), La Caixa Banking Foundation (LCF/BQ/PI20/11760025), Ministry of Science and Innovation (MCIN ; NextGenEU PCI2022-135018-2, MICIN ; FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEOand GenT ProgrammesGeneralitatValenciana (CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden: Swedish Research Council (VR 2018-00482, VR 202203845, VR 2022-04683, VR grant 2021-03651), Knut and Alice Wallenberg Foundation (KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, KAW 2019.0447); Switzerland: Swiss National Science Foundation (SNSF-PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004); USA: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer Family Foundation.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); BNL (USA); ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW; FWF, Austria; ANAS; CNPq; FAPESP, Brazil; NSERC; CFI, Canada; MOST; NSFC, China; MEYS CR, Czech Republic; DNRF; DNSRC, Denmark; IN2P3-CNRS; CEA-DRF/IRFU, France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ISF; Benoziyo Center, Israel; INFN, Italy; MEXT; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS; MIZS, Slovenia; MICINN, Spain; Wallenberg Foundation, Sweden; SNSF; MOST, Taipei; DOE; NSF; BCKDF; CANARIE; CRC; DRAC, Canada [PRIMUS 21/SCI/017, UNCE SCI/013]; Czech Republic; ERC; ERDF; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex; ANR, France; DFG; AvH Foundation, Germany; Thales; EU-ESF; Greek NSRF, Greece; BSF-NSF; NCN [UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187]; La Caixa Banking Foundation [LCF/BQ/PI20/11760025]; CERCA Programme Generalitat de Catalunya; PROMETEO; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society; Leverhulme Trust, United Kingdom; Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT) [1190886]; FONDECYT [1230987]; China: National Natural Science Foundation of China [NSFC-12175119, NSFC 12275265, NSFC-12075060]; Czech Republic: PRIMUS Research Programme [PRIMUS/21/SCI/017]; EU [ERC-101002463]; European Union: European Research Council [ERC-948254, MUCCA-CHIST-ERA-19-XAI-00]; European Union [FAIR-NextGenerationEU PE00000013]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC); Marie Sklodowska-Curie Actions (EU) [101033496]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022]; Investissements d'Avenir Idex [ANR-11-LABX-0012]; Investissements d'Avenir Labex; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG-469666862, DFG-CR 312/5-1, 754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [JP21H05085, JP22H01227, JP22H04944, JP22KK0227, NWO Veni 2020-VI]; Norway: Research Council of Norway [RCN-314472]; Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Polish National Science Centre (NCN) [2021/42/E/ST2/00350]; NCN OPUS [2022/47/B/ST2/03059]; Slovenian Research Agency [J1-3010]; BBVA Foundation [LEO22-1-603]; Generalitat Valenciana (Artemisa, FEDER) [IDIFEDER/2018/048]; Ministry of Science and Innovation [NextGenEU PCI2022-135018-2]; MICIN FEDER [PID2021-125273NB, RYC2019-028510-I, RYC2021-031273-I, RYC2022-038164-I, CIDEGENT/2019/023, CIDEGENT/2019/027]; Swedish Research Council [VR 2018-00482, VR 202203845, VR 2022-04683, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, KAW 2018.0458]; Swiss National Science Foundation [SNSF-PCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; USA: U.S. Department of Energy [ECA DE-AC02-76SF00515]; Neubauer Family Foundatio
Kifoplasti Balonu Yardımıyla Genişleyebilen Omurlararası Füzyon Kafesi Tasarımı, Sonlu Elemanlar Yöntemi ile Analizi ve Prototip Üretimi
No full textHer iki omur arasında, omurlar arasındaki etkiyi absorbe etmek için bir intervertebral disk bulunmaktadır . Ancak, zamanla intervertebral disk yaşlanma süreci nedeniyle dejenerasyona uğrar ve görevini yerine getiremez hale gelir. Dejeneratif intervertebral diskin yerini alması için spinal kafesler kullanılır. Bunların temel özellikleri omurgayı stabilize etmek ve hareket segmentindeki iki omur arasında yükseklik sağlamaktır. Temelde iki tür omurga kafesi bulunmaktadır: sabit ve genişletilebilir kafesler. Günümüzde, her bireyin farklı bir kemik yapısına sahip olması nedeniyle genişletilebilir kafeslerin kullanımı önemli ölçüde artmıştır. Bu kemik farklılığından dolayı, her bireydeki omurlar arası yüksekliğin farklı olması, ayarlanabilir olması önemli bir avantajdır.Kyphoplasti, omurganın çökmesi nedeniyle balon yardımıyla bu yüksekliği geri kazandırmak amacıyla gerçekleştirilen bir işlemdir. Kyphoplasti balonu, yüksekliği geri kazanmak için kullanılır. Bu tez kapsamında, kyphoplasti balonu kullanılarak tasarlanan bir genişletilebilir omurga kafesi tasarlanmıştır. Tez kapsamında tasarlananan omurga kafesi modeli, 3D modelleme programı olan NX Unigraphics kullanılarak tasarlanmıştır.Between each vertebra, there is an intervertebral disc to absorb the impact between the vertebrae. However, over time, the intervertebral disc degenerates due to the aging process and becomes unable to perform its function. Spinal cages are used to replace the degenerating intervertebral disc. Their main function is to stabilize the spine and provide height between the two vertebrae in the motion segment. There are basically two types of spinal cages: fixed and expandable cages. Today, the use of expandable cages has increased significantly due to the fact that each individual has a different bone structure. Due to this bone difference, the height between the vertebrae in each individual is different and can be adjusted, which is an important advantage Kyphoplasty is a procedure performed to restore this height with the help of a balloon due to the collapse of the spine. The kyphoplasty balloon is used to restore height. In this thesis, an expandable spine cage is designed using a kyphoplasty balloon. The spine cage model designed in this thesis was designed using the 3D modeling program NX Unigraphics
Calibration of a Soft Secondary Vertex Tagger Using Proton-Proton Collisions at √s=13 Tev With the Atlas Detector
No full textAad, Georges/0000-0002-6665-4934; Smirnova, Oxana/0000-0003-2517-531X; Canbay, Ali Can/0000-0003-4602-473X; Abbott, Braden/0000-0002-5888-2734; Tian, Yusong/0000-0001-8739-9250; D'Auria, Saverio/0000-0003-3393-6318; Aboulhorma, Asmaa/0000-0002-9987-2292; Mazzeo, Elena/0000-0002-8406-0195; Sala, Alessandro/0000-0003-0824-7326; Mitsou, Vasiliki A./0000-0002-1533-8886; Calafiura, Paolo/0000-0002-1692-1678; Ventura, Andrea/0000-0002-3368-3413; Nasella, Laura/0000-0002-4871-784X; Gwilliam, Carl/0000-0002-9401-5304; Abdelhameed, Sara/0000-0002-0287-5869; Stanislaus, Beojan/0000-0001-9007-7658; Fernandez-Martinez, Pablo/0000-0002-7818-6971; Carmignani, Joseph (Joe)/0000-0002-1705-1061; Abramowicz, Halina/0000-0001-5329-6640; Petersen, Troels/0000-0003-0221-3037; Fiorini, Luca/0000-0002-5070-2735; Abicht, Nils Julius/0000-0001-5763-2760; Rompotis, Nikolaos/0000-0003-2577-1875; Kretzschmar, Jan/0000-0002-8515-1355; Ragusa, Francesco/0000-0002-4064-0489; Moura Junior, Natanael Nunes/0000-0003-0828-6085; Carbone, Antonio/0000-0002-4117-3800; Herde, Hannah/0000-0001-8926-6734; Camplani, Alessandra/0000-0002-6386-9788Several processes studied by the ATLAS experiment at the Large Hadron Collider produce low-momentum b-flavored hadrons in the final state. This paper describes the calibration of a dedicated tagging algorithm that identifies b-flavored hadrons outside of hadronic jets by reconstructing the soft secondary vertices originating from their decays. The calibration is based on a proton-proton collision dataset at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 140 fb(-1). Scale factors used to correct the algorithm's performance in simulated events are extracted for the b-tagging efficiency and the mistag rate of the algorithm using a data sample enriched in t (t) over bar events. Several orthogonal measurement regions are defined, binned as a function of the multiplicities of soft secondary vertices and jets containing a b-flavored hadron in the event. The mistag rate scale factors are estimated separately for events with low and high average numbers of interactions per bunch crossing. The results, which are derived from events with low missing transverse momentum, are successfully validated in a phase space characterized by high missing transverse momentum and therefore are applicable to new physics searches carried out in either phase space regime.We thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide without whom ATLAS could not be operated efficiently. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK), and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [76]. We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC, and CFI, Canada; CERN; ANID, Chile; CAS, MOST, and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF, and Cantons of Bern and Geneva, Switzerland; MOST, Taipei; TENMAK, Turkiye; STFC, United Kingdom; DOE and NSF, U.S. Individual groups and members have received support from BCKDF, CANARIE, CRC, and DRAC, Canada; CERN-CZ, FORTE, and PRIMUS, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU, and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex, and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales, and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya, and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; and The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1190886, FONDECYT 1230812, FONDECYT 1230987); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700), National Natural Science Foundation of China (NSFC-12175119, NSFC-12275265, NSFC-12075060); Czech Republic: Czech Science Foundation (GACR-24-11373S), Ministry of Education Youth and Sports (FORTE CZ.02.01. 01/00/22_008/0004632), PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC101002463); European Union: European Research Council (ERC-948254, ERC-101089007), Horizon 2020 Framework Programme (MUCCA-CHIST-ERA-19-XAI-00), European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU PE00000013), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG-469666862, DFG-CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP21H05085, JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472); Poland: Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085); Slovenia: Slovenian Research Agency (ARIS Grant No. J1-3010); Spain: Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), Ministry of Science and Innovation (MCIN and NextGenEU PCI2022-135018-2, MICIN and FEDER PID2021-125273NB, RYC2019028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden: Swedish Research Council (Swedish Research Council 2023-04654, VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 2023-03403, VR Grant No. 2021-03651), Knut and Alice Wallenberg Foundation (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSF-PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004), Royal Society (NIF-R1-231091); U.S.: U.S. Department of Energy (ECA DEAC02-76SF00515), Neubauer Family Foundation.ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerba.an; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; ANID, Chile; CAS, China; MOST, China; NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRI, Greece; RGC, China; Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARIS, Slovenia; MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC , Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; NSTC, Taipei; TENMAK, Turkiye; STFC/UKRI, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; CRC, Canada; DRAC, Canada; CERN-CZ, Czech Republic; FORTE, Czech Republic; PRIMUS, Czech Republic; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; ICSC-NextGenerationEU, European Union; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France; Investissements d'Avenir Idex, France; ANR, France; DFG, Germany; AvH Foundation, Germany; Herakleitos programme - by EU-ESF, Greece; Thales programme - by EU-ESF, Greece; Aristeia programme - by EU-ESF, Greece; Greek NSRF, Greece; BSF-NSF, Israel; MINERVA, Israel; NCN, Poland; NAWA, Poland; La Caixa Banking Foundation, Spain; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; GenT Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; Yerevan Physics Institute (FAPERJ); European Organization for Nuclear Research (CERN PJAS); Agencia Nacional de Investigacion y Desarrollo [FONDECYT 1230812, FONDECYT 1230987, FONDECYT 1240864]; Chinese Ministry of Science and Technology [MOST-2023YFA1605700]; National Natural Science Foundation of China [NSFC -12175119, NSFC 12275265, NSFC-12075060]; Czech Science Foundation [GACR-24-11373S]; Ministry of Education Youth and Sports [FORTE CZ.02.01.01/00/22_008/0004632]; PRIMUS Research Programme [PRIMUS/21/SCI/017]; H2020 European Research Council [ERC -101002463]; European Research Council [ERC-948254, ERC 101089007]; Horizon 2020 Framework Programme [MUCCA-CHIST-ERA-19-XAI-00]; European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU) [PE00000013]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002]; Investissements d'Avenir Labex [ANR-11-LABX-0012]; Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG -469666862, DFG -CR 312/5-2]; Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Universita e della Ricerca [PRIN-20223N7F8K-PNRR M4.C2.1.1]; Japan Society for the Promotion of Science [JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227, JSPS KAKENHI JP23KK0245]; Netherlands Organisation for Scientific Research [NWO Veni 2020 -VI.Veni.202.179]; Research Council of Norway [RCN-314472]; Ministry of Science and Higher Education [9722]; Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Polish National Science Centre [NCN 2021/42/E/ST2/00350, 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085]; Slovenian Research Agency (ARIS grant) [J1-3010]; Generalitat Valenciana (Artemisa, FEDER) [IDIFEDER/2018/048]; Ministry of Science and Innovation (MCIN) [NextGenEU PCI2022-135018-2, PID2021125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I]; PROMETEO Programme Generalitat Valenciana [CIDEGENT/2019/027]; GenT Programme Generalitat Valenciana [CIDEGENT/2019/027]; Swedish Research Council [VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 2023-03403, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW2018.0157, KAW2018.0458, KAW2019.0447, KAW2022.0358]; Swiss National Science Foundation [SNSF-PCEFP2_194658]; Leverhulme Trust [RPG-2020-004]; Royal Society [NIF-R1-231091]; U.S. Department of Energy [ECA DE-AC02-76SF00515]; Neubauer Family Foundatio
Achieving Beamfocusing via Two Separated Uniform Linear Arrays
No full textThis paper investigates coordinated beamforming using a modular linear array (MLA), composed of a pair of physically separated uniform linear arrays (ULAs), treated as sub-arrays. We focus on how such setups can give rise to near-field effects in 6G networks without requiring many antennas. Unlike conventional far-field beamforming, near-field beamforming enables simultaneous data service to multiple users at different distances in the same angular direction, offering significant multiplexing gains. We present a detailed analysis, including analytical expressions of the beamwidth and beamdepth for the MLA. Our findings reveal that using the MLA approach, we can remove approximately 36% of the antennas in the ULA while achieving the same level of beamfocusing.Swedish Foundation for Strategic Research [FFL18-0277]This work was supported by the FFL18-0277 grant from the Swedish Foundation for Strategic Research
Using Pile-Up Collisions as an Abundant Source of Low-Energy Hadronic Physics Processes in Atlas and an Extraction of the Jet Energy Resolution
No full textDuring the 2015–2018 data-taking period, the Large Hadron Collider delivered proton-proton bunch crossings at a centre-of-mass energy of 13 TeV to the ATLAS experiment at a rate of roughly 30 MHz, where each bunch crossing contained an average of 34 independent inelastic proton-proton collisions. The ATLAS trigger system selected roughly 1 kHz of these bunch crossings to be recorded to disk. Offline algorithms then identify one of the recorded collisions as the collision of interest for subsequent data analysis, and the remaining collisions are referred to as pile-up. Pile-up collisions represent a trigger-unbiased dataset, which is evaluated to have an integrated luminosity of 1.33 pb−1 in 2015–2018. This is small compared with the normal trigger-based ATLAS dataset, but when combined with vertex-by-vertex jet reconstruction it provides up to 50 times more dijet events than the conventional single-jet-trigger-based approach, and does so without adding any additional cost or requirements on the trigger system, readout, or storage. The pile-up dataset is validated through comparisons with a special trigger-unbiased dataset recorded by ATLAS, and its utility is demonstrated by means of a measurement of the jet energy resolution in dijet events, where the statistical uncertainty is significantly reduced for jet transverse momenta below 65 GeV. © The Author(s) 2024.Ministerio de Ciencia, Innovación y Universidades, MCIU; BSF-NSF; Australian Research Council, ARC; DRAC; La Caixa Banking Foundation; BMWFW; Centre National pour la Recherche Scientifique et Technique, CNRST; Fundação para a Ciência e a Tecnologia, FCT; European Union, Future Artificial Intelligence Research; National Science Foundation, NSF; CEA-DRF; Science and Technology Facilities Council, STFC; Horizon 2020, ICSC-NextGenerationEU; H2020 Marie Skłodowska-Curie Actions, MSCA; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, FAPERJ; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministry of Science and Technology, Taiwan, MOST; Israel Science Foundation, ISF; Wallenberg Foundation; Baden-Württemberg Stiftung, BWS; MVZI; PROMETEO; Neubauer Family Foundation, NFF; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; The Slovenian Research and Innovation Agency, ARRS; IDUB AGH; Generalitat de Catalunya; Instituto Nazionale di Fisica Nucleare, INFN; Austrian Science Fund, FWF; Yerevan Physics Institute; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Canada Foundation for Innovation, CFI; Helmholtz-Gemeinschaft, HGF; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Canarie; Horizon 2020 Framework Programme, H2020; Göran Gustafssons Stiftelser; European Commission, EC; European Cooperation in Science and Technology, COST; EU-ESF; International Council of Shopping Centers, ICSC; RGC; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; PRIMUS; Agencia Estatal de Investigación, AEI; Institutul de Fizică Atomică, IFA; Natural Sciences and Engineering Research Council of Canada, NSERC; National Science and Technology Council, NSTC; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; GenT Programmes Generalitat Valenciana, Spain; Irish Rugby Football Union, IRFU; Cantons of Bern and Geneva; Chinese Academy of Sciences, CAS; Defence Science Institute, DSI; CRC Health Group, CRC; MSTDI; MNE; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Minerva Foundation; CERN-CZ; National Research Foundation, NRF; Ministerstwo Edukacji i Nauki, MNiSW; Generalitat Valenciana, GVA; CERN; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; National Research Council Canada, NRC; Alexander von Humboldt-Stiftung, AvH; Multiple Sclerosis Scientific Research Foundation, MSSRF; British Columbia Knowledge Development Fund, BCKDF; Ministry of Education, Culture, Sports, Science and Technology, MEXT; UK Research and Innovation, UKRI; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF, (SNSF – PCEFP2_194658); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; European Regional Development Fund, ERDF, (IDIFEDER/2018/048); European Regional Development Fund, ERDF; Leverhulme Trust, (RPG-2020-004); Leverhulme Trust; Center for Advancing Research Impact in Society, ARIS, (J1-3010); Center for Advancing Research Impact in Society, ARIS; European Research Council, ERC, (101089007, 948254); European Research Council, ERC; FAIR-NextGenerationEU, (PE00000013); Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR, (PRIN – 20223N7F8K – PNRR M4.C2.1.1); Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; Knut och Alice Wallenbergs Stiftelse, (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Knut och Alice Wallenbergs Stiftelse; Norges Forskningsråd, (RCN-314472); Norges Forskningsråd; Deutsche Forschungsgemeinschaft, DFG, (DFG – CR 312/5-2, DFG – 469666862); Deutsche Forschungsgemeinschaft, DFG; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT, (1240864, 1230987, 1230812); Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT; Narodowe Centrum Nauki, NCN, (UMO-2021/40/C/ST2/00187, UMO-2019/34/E/ST2/00393, 2022/47/B/ST2/03059, UMO-2023/51/B/ST2/00920, 2021/42/E/ST2/00350, UMO-2023/49/B/ST2/04085, UMO-2022/47/O/ST2/00148, UMO-2020/37/B/ST2/01043); Narodowe Centrum Nauki, NCN; Investissements d’Avenir Labex, (ANR-11-LABX-0012); Japan Society for the Promotion of Science, JSPS, (JP22KK0227, JP22H04944, JP23KK0245, JP22H01227); Japan Society for the Promotion of Science, JSPS; DNSRC, (IN2P3-CNRS); Agence Nationale de la Recherche, ANR, (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-22-EDIR-0002, ANR-21-CE31-0022); Agence Nationale de la Recherche, ANR; GenT Programmes Generalitat Valenciana, (CIDEGENT/2019/027); Vetenskapsrådet, VR, (2023-04654, VR 2023-03403, VR 2022-04683, 2021-03651, VR 2018-00482, VR 2022-03845); Vetenskapsrådet, VR; Forskningsrådet för hälsa, arbetsliv och välfärd, FORTE, (CZ.02.01.01/00/22_008/0004632, PRIMUS/21/SCI/017); Forskningsrådet för hälsa, arbetsliv och välfärd, FORTE; National Natural Science Foundation of China, NSFC, (12275265, NSFC-12075060, NSFC – 12175119); National Natural Science Foundation of China, NSFC; Royal Society, (NIF-R1-231091); Royal Society; Narodowa Agencja Wymiany Akademickiej, NAWA, (PPN/PPO/2020/1/00002/U/00001); Narodowa Agencja Wymiany Akademickiej, NAWA; Ministerio de Ciencia e Innovación, MCIN, (RYC2019-028510-I, RYC2021-031273-I, PID2021-125273NB, RYC2022-038164-I, RYC2020-030254-I, PCI2022-135018-2); Ministerio de Ciencia e Innovación, MCIN; Grantová Agentura České Republiky, GAČR, (GACR – 24-11373S); Grantová Agentura České Republiky, GAČR; Ministry of Science and Technology of the People's Republic of China, MOST, (MOST-2023YFA1605700); Ministry of Science and Technology of the People's Republic of China, MOST; U.S. Department of Energy, USDOE, (ECA DE-AC02-76SF00515); U.S. Department of Energy, USDOE; H2020 European Research Council, ERC, (ERC – 101002463); H2020 European Research Council, ERC; MUCCA, (CHIST-ERA-19-XAI-00
Kompozit Kanat Yapıları Üzerinde Tamir Modellemesi ve Sonlu Elemanlar Analizi
No full textCarbon fiber reinforced polymers (CFRP) are widely used in the aviation industry. These materials have properties such as high strength-to-weight ratio, fatigue resistance, lightweight, and corrosion resistance. However, a significant disadvantage of these composite structures is their susceptibility to damage due to low impact toughness. It is known that when such damage occurs, it may necessitate the repair or replacement of the structure. Repair designs of composite structures can provide strength repair and enable relatively cost-effective repair of damaged parts by creating aerodynamically smooth surfaces. Therefore, parameters such as layer orientation, thickness, shape, extra layer count, and repair area size should be selected according to requirements. In this thesis, repair designs on a full-scale composite wing structure were modeled and the structural integrity of the structure was evaluated under realistic loading conditions. Repair modeling and analyses were performed using the shell method on the composite structure. To validate the shell method, tensile tests were conducted at the specimen level according to ASTM-3039D standards. The results obtained from the tests conducted at the specimen level corroborated the structural analysis results. Structural analyses were performed using ABAQUS software. In the continuation of the thesis study, computational fluid dynamics (CFD) analyses of a new wing model developed based on the NACA wing profiles and designed according to determined requirements were performed using ANSYS Fluent software. CFD analyses were conducted considering angles of attack of 0°-5°-10°-15°-20°-25°-30° on the designed wing profile. The pressure values obtained at the most critical angle of attack from the CFD analyses were determined using the load distribution function for use in structural analyses. In addition to aerodynamic forces, forces such as gravity, ammunition weight, and fuel weight were applied as boundary conditions to the full scale wing model. Various repair scenarios were identified, and structural analyses of repairs were performed on the most critical region of the full-scale wing shell structure. The repair scenarios applied in the determined region were compared with strain values using the Hashin method, and the repair works were validated. The results obtained showed that factors such as the number of damaged areas in the structure, the number of damaged layers, the number and orientation of extra layers applied to the repair area, and the shape of the repair design affect the load-carrying capacity and load path of the structure. The conducted numerical analysis studies showed that repair modeling using the shell method provided accurate results and provided important information about the method to be followed in case of possible damage.Karbon fiber takviyeli polimerler (CFRP), havacılık endüstrisinde yaygın olarak kullanılmaktadır. Bu malzemeler yüksek mukavemet/ağırlık oranı, yorulma direnci, hafiflik ve korozyon direnci gibi özelliklere sahiptirler. Ancak, bu kompozit yapıların önemli bir dezavantajı, düşük darbe tokluğundan kaynaklanan hasara duyarlı olmalarıdır. Bu tür bir hasar meydana geldiğinde, yapının tamir veya değiştirme gerektirebileceği bilinmektedir. Kompozit yapıların tamir tasarımları, mukavemet onarımını sağlayabilir ve aerodinamik açıdan düzgün yüzeyler oluşturarak hasarlı parçanın nispeten daha maliyet etkin bir şekilde onarılmasına olanak tanır. Bu nedenle, tabakaların oryantasyonu, kalınlığı, şekli, ekstra tabaka sayısı, tamir bölgesinin büyüklüğü gibi parametreler gereksinimlere uygun olarak seçilmelidir. Bu tez çalışması kapsamında, tam ölçekli bir kabuk yapısındaki kompozit kanat üzerinde tamir tasarımları modellenmiş ve gerçekçi yüklenme koşulları altında yapının yapısal bütünlüğü değerlendirilmiştir. Tamir modellemesi ve analizleri kompozit yapı üzerinde kabuk metodu kullanılarak gerçekleştirilmiştir. Kabuk metodunun doğrulanması amacıyla numune seviyesinde ASTM-3039D standartlarına uygun çekme testleri gerçekleştirilmiştir. Numune seviyesinde gerçekleştirilen testlerden elde edilen sonuçlar ve yapısal analiz sonuçlarını doğrulamıştır. Yapısal analizler, ABAQUS yazılımı kullanılarak gerçekleştirilmiştir. Tez çalışmasının devamında, NACA kanat profillerine dayalı olarak geliştirilen ve belirlenen gereksinimlere uygun olarak tasarlanmış yeni bir kanat modelinin hesaplamalı akışkanlar dinamiği (HAD) analizleri ANSYS Fluent yazılımı kullanılarak gerçekleştirilmiştir. Tasarlanan kanat profili üzerinde 0°-5°-10°-15°-20°-25°-30° değerlerindeki hücum açıları düşünülerek HAD analizleri gerçekleştirilmiştir. HAD analizlerinden en kritik hücum açısında elde edilen basınç değerleri, yapısal analizlerde kullanılmak üzere yük dağılımı fonksiyonu kullanılarak belirlenmiştir. Aerodinamik kuvvetlere ek olarak yer çekimi, mühimmat ağırlığı ve yakıt ağırlığı gibi kuvvetler, tam ölçekli kanat modeline sınır şartı olarak uygulanmıştır. Çeşitli tamir senaryoları belirlenmiş ve tam ölçekli kanat kabuk yapısındaki en kritik bölgede tamirlerin yapısal analizleri gerçekleştirilmiştir. Belirlenen bölgede uygulanan tamir senaryoları Hashin metodunu kullanılarak gerinim değerleri ile kıyaslanmış ve tamir çalışmaları doğrulanmıştır. Elde edilen sonuçlar, yapıdaki hasarlı bölge sayısı, hasarlı tabaka sayısı, tamir bölgesine serimi uygulanan ekstra tabaka sayısı ve oryantasyonu, tamir tasarımının şekli gibi faktörlerin yapının yük taşıma kapasitesini ve yük yolunu etkilediğini göstermiştir. Gerçekleştirilen sayısal analiz çalışmaları, kabuk yöntemi ile tamir modellemesinin isabetli sonuçlar sağladığını göstermiştir ve olası hasar durumlarında izlenilmesi gereken yöntem hakkında önemli bilgiler sunmuştur
